{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Constructing a NEP model\n",
    "## Introduction\n",
    "\n",
    "In this tutorial, we will show how to use the ``nep`` executable to train a NEP potential and check the results.\n",
    "For background on the NEP approach see [this section of the documentation](https://gpumd.org/potentials/nep).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "You need to have access to an Nvidia GPU with compute capability >= 3.5. CUDA 9.0 or higher is also needed to compile the GPUMD package.\n",
    "You can download GPUMD, unpack it, go to the ``src`` directory, and type ``make -j`` to compile. Then, you should get two executables: ``gpumd`` and ``nep`` in the ``src`` directory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a NEP potential for PbTe\n",
    "To train a NEP potential, one must prepare at least two input files: ``train.xyz`` and ``nep.in``. The ``train.xyz`` file contains all the training data, and the `nep.in` file contains some controlling parameters. One can also optionally provide a ``test.xyz`` file that contains all the testing data.\n",
    "\n",
    "### Prepare the train.xyz and test.xyz files\n",
    "- The ``train.xyz``/``test.xyz`` file should be prepared according to the [documentation](https://gpumd.org/nep/input_files/).\n",
    "- For the example in this tutorial, we have already prepared the ``train.xyz`` and ``test.xyz`` files in the current folder, which contain 25 and 300 structures, respectively.\n",
    "- In this example, there are only energy and force training data. In general, one can also have virial training data.\n",
    "- We used the VASP code to calculate the training data, but ``nep`` does not care about this. You can generate the data in ``train.xyz``/``test.xyz`` using any method that works for you. For example, before doing new DFT calculations, you can first check if there are training data publicly available and then try to convert them into the format as required by ``train.xyz``/``test.xyz``. There are quite a few tools (https://github.com/brucefan1983/GPUMD/tree/master/tools) available for this purpose."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare the `nep.in` file. \n",
    "- First, study the [documentation about this input file](https://gpumd.org/nep/input_files/).\n",
    "- For the example in this tutorial, we have prepared the ``nep.in`` file in the current folder. \n",
    "- The ``nep.in`` file reads:\n",
    "```\n",
    "type         2 Te Pb\n",
    "```\n",
    "This is the simplest ``nep.in`` file, with only one keyword, which means that there are $N_{\\rm typ}=2$ atom types in the training set: Te and Pb. The user can also write Pb first and Te next. The code will remember the order of the atom types here and record it into the NEP potential file ``nep.txt``. Therefore, the user does not need to remember the order of the atom types here. All the other parameters will have good default values (please check the screen output).\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the ``nep`` executable\n",
    " - Go to the current folder from command line and type\n",
    "```\n",
    "path/to/nep # linux\n",
    "path\\to\\nep # Windows\n",
    "```\n",
    " \n",
    "It took about 12 minutes to run 10,000 generations using my laptop with a GeForce RTX 2070 GPU card. One can kill the executable at any time and also restart it by simply running the executable again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check the training results\n",
    "\n",
    "- After running the `nep` executable, there will be some output on the screen. We encourage the user to read the screen output carefully. It can help to understand the calculation flow of `nep`. \n",
    "- Some files will be generated in the current folder and will be updated every 100 generations (some will be updated every 1000 generations). Therefore, one can check the results even before finishing the maximum number of generations as set in `nep.in`. \n",
    "- If the user has not studied the documentation of the output files generated by `nep`, it is time to read about those [here](https://gpumd.org/nep/output_files/).\n",
    "- We will use Python to visualize the results in some of the output files next. We first load ``pylab`` that we need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the ``loss.out`` file\n",
    "- We see that the $\\mathcal{L}_1$ and $\\mathcal{L}_2$ regularization loss functions first increases and then decreases, which indicates the effectiveness of the regularization.\n",
    "- The energy loss is the root mean square error (RMSE) of energy per atom, which converges to about 0.4 meV/atom.\n",
    "- The force loss is the RMSE of force components, which converges to about 40 meV/Å."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = loadtxt('loss.out')\n",
    "loglog(loss[:, 1:6])\n",
    "loglog(loss[:, 7:9])\n",
    "xlabel('Generation/100')\n",
    "ylabel('Loss')\n",
    "legend(['Total', 'L1-regularization', 'L2-regularization', 'Energy-train', 'Force-train', 'Energy-test', 'Force-test'])\n",
    "tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the `energy_test.out` file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dots are the raw data and the line represents the identity function used to guide the eyes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy_test = loadtxt('energy_test.out')\n",
    "plot(energy_test[:, 1], energy_test[:, 0], '.')\n",
    "plot(linspace(-3.85,-3.69), linspace(-3.85,-3.69), '-')\n",
    "xlabel('DFT energy (eV/atom)')\n",
    "ylabel('NEP energy (eV/atom)')\n",
    "tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the `force_test.out` file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dots are the raw data and the line represents the identity function used to guide the eyes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "force_test = loadtxt('force_test.out')\n",
    "plot(force_test[:, 3:6], force_test[:, 0:3], '.')\n",
    "plot(linspace(-4,4), linspace(-4,4), '-')\n",
    "xlabel('DFT force (eV/A)')\n",
    "ylabel('NEP force (eV/A)')\n",
    "legend(['x direction', 'y direction', 'z direction'])\n",
    "tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the `virial_test.out` file\n",
    "In general, one can similarly check the `virial_test.out` file, but in this particular example, virial data were not used in the training process (no virial data exist in ``train.xyz``/``test.xyz``). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the other files\n",
    "One can similarly check the other files: ``energy_train.out``, ``force_train.out``, and ``virial_train.out``. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Restart\n",
    "- After each 100 steps, the ``nep.restart`` file will be updated.\n",
    "- If ``nep.restart`` exists, it means you want to continue the previous training. Remember not to change the parameters related to the descriptor and the number of neurons. Otherwise the restarting behavior is undefined.\n",
    "- If you want to train from scratch, you need to delete ``nep.restart`` first (better to first make a copy of all the results from the previous training)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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